{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from NonLocalNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = NonLocalNet().to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 3.9525 Top-1 准确率: 0.0954 Top-5 准确率: 0.2890\n",
      "val 损失: 4.1093 Top-1 准确率: 0.1075 Top-5 准确率: 0.3127\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.1811 Top-1 准确率: 0.2155 Top-5 准确率: 0.5065\n",
      "val 损失: 3.1044 Top-1 准确率: 0.2463 Top-5 准确率: 0.5498\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6700 Top-1 准确率: 0.3084 Top-5 准确率: 0.6348\n",
      "val 损失: 3.1909 Top-1 准确率: 0.2495 Top-5 准确率: 0.5515\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3352 Top-1 准确率: 0.3817 Top-5 准确率: 0.7096\n",
      "val 损失: 2.3270 Top-1 准确率: 0.3875 Top-5 准确率: 0.7098\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1034 Top-1 准确率: 0.4339 Top-5 准确率: 0.7572\n",
      "val 损失: 2.4323 Top-1 准确率: 0.3851 Top-5 准确率: 0.7121\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9225 Top-1 准确率: 0.4742 Top-5 准确率: 0.7914\n",
      "val 损失: 2.3176 Top-1 准确率: 0.4152 Top-5 准确率: 0.7217\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7628 Top-1 准确率: 0.5127 Top-5 准确率: 0.8180\n",
      "val 损失: 2.2508 Top-1 准确率: 0.4282 Top-5 准确率: 0.7572\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6381 Top-1 准确率: 0.5443 Top-5 准确率: 0.8394\n",
      "val 损失: 1.9386 Top-1 准确率: 0.4890 Top-5 准确率: 0.7948\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5375 Top-1 准确率: 0.5637 Top-5 准确率: 0.8574\n",
      "val 损失: 1.8126 Top-1 准确率: 0.5129 Top-5 准确率: 0.8127\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4435 Top-1 准确率: 0.5926 Top-5 准确率: 0.8694\n",
      "val 损失: 1.6407 Top-1 准确率: 0.5479 Top-5 准确率: 0.8409\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3713 Top-1 准确率: 0.6098 Top-5 准确率: 0.8811\n",
      "val 损失: 1.5265 Top-1 准确率: 0.5713 Top-5 准确率: 0.8617\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3037 Top-1 准确率: 0.6240 Top-5 准确率: 0.8903\n",
      "val 损失: 1.6699 Top-1 准确率: 0.5472 Top-5 准确率: 0.8403\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2433 Top-1 准确率: 0.6432 Top-5 准确率: 0.9000\n",
      "val 损失: 1.5387 Top-1 准确率: 0.5764 Top-5 准确率: 0.8566\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1861 Top-1 准确率: 0.6582 Top-5 准确率: 0.9069\n",
      "val 损失: 1.4912 Top-1 准确率: 0.5871 Top-5 准确率: 0.8652\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1360 Top-1 准确率: 0.6718 Top-5 准确率: 0.9147\n",
      "val 损失: 1.3539 Top-1 准确率: 0.6231 Top-5 准确率: 0.8825\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0914 Top-1 准确率: 0.6831 Top-5 准确率: 0.9190\n",
      "val 损失: 1.5227 Top-1 准确率: 0.5955 Top-5 准确率: 0.8664\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0451 Top-1 准确率: 0.6936 Top-5 准确率: 0.9249\n",
      "val 损失: 1.3046 Top-1 准确率: 0.6357 Top-5 准确率: 0.8887\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9995 Top-1 准确率: 0.7074 Top-5 准确率: 0.9316\n",
      "val 损失: 1.2618 Top-1 准确率: 0.6438 Top-5 准确率: 0.8928\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9583 Top-1 准确率: 0.7186 Top-5 准确率: 0.9366\n",
      "val 损失: 1.5547 Top-1 准确率: 0.5849 Top-5 准确率: 0.8594\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9305 Top-1 准确率: 0.7247 Top-5 准确率: 0.9392\n",
      "val 损失: 1.2673 Top-1 准确率: 0.6470 Top-5 准确率: 0.8910\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8830 Top-1 准确率: 0.7377 Top-5 准确率: 0.9446\n",
      "val 损失: 1.2354 Top-1 准确率: 0.6539 Top-5 准确率: 0.9008\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8576 Top-1 准确率: 0.7460 Top-5 准确率: 0.9471\n",
      "val 损失: 1.2346 Top-1 准确率: 0.6572 Top-5 准确率: 0.9019\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8267 Top-1 准确率: 0.7544 Top-5 准确率: 0.9512\n",
      "val 损失: 1.2275 Top-1 准确率: 0.6596 Top-5 准确率: 0.9054\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8009 Top-1 准确率: 0.7640 Top-5 准确率: 0.9533\n",
      "val 损失: 1.2749 Top-1 准确率: 0.6518 Top-5 准确率: 0.8960\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7737 Top-1 准确率: 0.7693 Top-5 准确率: 0.9572\n",
      "val 损失: 1.1718 Top-1 准确率: 0.6756 Top-5 准确率: 0.9058\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7478 Top-1 准确率: 0.7772 Top-5 准确率: 0.9597\n",
      "val 损失: 1.2435 Top-1 准确率: 0.6558 Top-5 准确率: 0.8994\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7128 Top-1 准确率: 0.7863 Top-5 准确率: 0.9627\n",
      "val 损失: 1.2419 Top-1 准确率: 0.6652 Top-5 准确率: 0.8999\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6866 Top-1 准确率: 0.7962 Top-5 准确率: 0.9654\n",
      "val 损失: 1.1670 Top-1 准确率: 0.6827 Top-5 准确率: 0.9106\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6692 Top-1 准确率: 0.7991 Top-5 准确率: 0.9671\n",
      "val 损失: 1.1926 Top-1 准确率: 0.6730 Top-5 准确率: 0.9050\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6434 Top-1 准确率: 0.8061 Top-5 准确率: 0.9695\n",
      "val 损失: 1.1439 Top-1 准确率: 0.6827 Top-5 准确率: 0.9149\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6275 Top-1 准确率: 0.8128 Top-5 准确率: 0.9703\n",
      "val 损失: 1.1679 Top-1 准确率: 0.6832 Top-5 准确率: 0.9093\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6044 Top-1 准确率: 0.8176 Top-5 准确率: 0.9732\n",
      "val 损失: 1.1456 Top-1 准确率: 0.6859 Top-5 准确率: 0.9166\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5795 Top-1 准确率: 0.8263 Top-5 准确率: 0.9753\n",
      "val 损失: 1.1754 Top-1 准确率: 0.6834 Top-5 准确率: 0.9085\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5696 Top-1 准确率: 0.8291 Top-5 准确率: 0.9766\n",
      "val 损失: 1.1049 Top-1 准确率: 0.6967 Top-5 准确率: 0.9146\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5367 Top-1 准确率: 0.8392 Top-5 准确率: 0.9783\n",
      "val 损失: 1.1129 Top-1 准确率: 0.6951 Top-5 准确率: 0.9186\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5243 Top-1 准确率: 0.8436 Top-5 准确率: 0.9788\n",
      "val 损失: 1.1889 Top-1 准确率: 0.6803 Top-5 准确率: 0.9089\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5100 Top-1 准确率: 0.8457 Top-5 准确率: 0.9811\n",
      "val 损失: 1.1246 Top-1 准确率: 0.7002 Top-5 准确率: 0.9190\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4950 Top-1 准确率: 0.8506 Top-5 准确率: 0.9819\n",
      "val 损失: 1.0435 Top-1 准确率: 0.7137 Top-5 准确率: 0.9238\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4736 Top-1 准确率: 0.8587 Top-5 准确率: 0.9838\n",
      "val 损失: 1.0878 Top-1 准确率: 0.7024 Top-5 准确率: 0.9231\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4628 Top-1 准确率: 0.8616 Top-5 准确率: 0.9839\n",
      "val 损失: 1.1046 Top-1 准确率: 0.7051 Top-5 准确率: 0.9201\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4516 Top-1 准确率: 0.8639 Top-5 准确率: 0.9844\n",
      "val 损失: 1.0784 Top-1 准确率: 0.7101 Top-5 准确率: 0.9236\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4359 Top-1 准确率: 0.8696 Top-5 准确率: 0.9864\n",
      "val 损失: 1.1090 Top-1 准确率: 0.7019 Top-5 准确率: 0.9195\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4200 Top-1 准确率: 0.8752 Top-5 准确率: 0.9861\n",
      "val 损失: 1.1854 Top-1 准确率: 0.6992 Top-5 准确率: 0.9145\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3986 Top-1 准确率: 0.8809 Top-5 准确率: 0.9879\n",
      "val 损失: 1.1446 Top-1 准确率: 0.7027 Top-5 准确率: 0.9194\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3946 Top-1 准确率: 0.8831 Top-5 准确率: 0.9878\n",
      "val 损失: 1.1706 Top-1 准确率: 0.7043 Top-5 准确率: 0.9198\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3830 Top-1 准确率: 0.8852 Top-5 准确率: 0.9890\n",
      "val 损失: 1.1631 Top-1 准确率: 0.7022 Top-5 准确率: 0.9203\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3756 Top-1 准确率: 0.8879 Top-5 准确率: 0.9895\n",
      "val 损失: 1.1043 Top-1 准确率: 0.7117 Top-5 准确率: 0.9189\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3614 Top-1 准确率: 0.8924 Top-5 准确率: 0.9903\n",
      "val 损失: 1.0922 Top-1 准确率: 0.7096 Top-5 准确率: 0.9245\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3628 Top-1 准确率: 0.8912 Top-5 准确率: 0.9897\n",
      "val 损失: 1.1268 Top-1 准确率: 0.7116 Top-5 准确率: 0.9213\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3467 Top-1 准确率: 0.8964 Top-5 准确率: 0.9909\n",
      "val 损失: 1.0688 Top-1 准确率: 0.7215 Top-5 准确率: 0.9260\n",
      "\n",
      "训练完成，耗时 123m 20s\n",
      "最佳验证集准确率: 0.721500\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 0.2038 Top-1 准确率: 0.9467 Top-5 准确率: 0.9960\n",
      "val 损失: 0.8832 Top-1 准确率: 0.7627 Top-5 准确率: 0.9412\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1575 Top-1 准确率: 0.9613 Top-5 准确率: 0.9968\n",
      "val 损失: 0.8661 Top-1 准确率: 0.7664 Top-5 准确率: 0.9435\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1420 Top-1 准确率: 0.9652 Top-5 准确率: 0.9974\n",
      "val 损失: 0.8620 Top-1 准确率: 0.7690 Top-5 准确率: 0.9432\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1291 Top-1 准确率: 0.9696 Top-5 准确率: 0.9979\n",
      "val 损失: 0.8605 Top-1 准确率: 0.7676 Top-5 准确率: 0.9432\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1209 Top-1 准确率: 0.9719 Top-5 准确率: 0.9981\n",
      "val 损失: 0.8574 Top-1 准确率: 0.7700 Top-5 准确率: 0.9442\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1161 Top-1 准确率: 0.9738 Top-5 准确率: 0.9981\n",
      "val 损失: 0.8537 Top-1 准确率: 0.7716 Top-5 准确率: 0.9436\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1109 Top-1 准确率: 0.9749 Top-5 准确率: 0.9981\n",
      "val 损失: 0.8562 Top-1 准确率: 0.7706 Top-5 准确率: 0.9445\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1093 Top-1 准确率: 0.9746 Top-5 准确率: 0.9980\n",
      "val 损失: 0.8515 Top-1 准确率: 0.7746 Top-5 准确率: 0.9434\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1082 Top-1 准确率: 0.9752 Top-5 准确率: 0.9983\n",
      "val 损失: 0.8575 Top-1 准确率: 0.7702 Top-5 准确率: 0.9437\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1010 Top-1 准确率: 0.9774 Top-5 准确率: 0.9983\n",
      "val 损失: 0.8566 Top-1 准确率: 0.7713 Top-5 准确率: 0.9427\n",
      "\n",
      "训练完成，耗时 13m 56s\n",
      "最佳验证集准确率: 0.774600\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs= 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model2, 'NoneLocal.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
